Quantitative Finance > Statistical Finance

Abstract: Prediction of future movement of stock prices has been a subject matter of
many research work. In this work, we propose a hybrid approach for stock price
prediction using machine learning and deep learning-based methods. We select
the NIFTY 50 index values of the National Stock Exchange of India, over a
period of four years, from January 2015 till December 2019. Based on the NIFTY
data during the said period, we build various predictive models using machine
learning approaches, and then use those models to predict the Close value of
NIFTY 50 for the year 2019, with a forecast horizon of one week. For predicting
the NIFTY index movement patterns, we use a number of classification methods,
while for forecasting the actual Close values of NIFTY index, various
regression models are built. We, then, augment our predictive power of the
models by building a deep learning-based regression model using Convolutional
Neural Network with a walk-forward validation. The CNN model is fine-tuned for
its parameters so that the validation loss stabilizes with increasing number of
iterations, and the training and validation accuracies converge. We exploit the
power of CNN in forecasting the future NIFTY index values using three
approaches which differ in number of variables used in forecasting, number of
sub-models used in the overall models and, size of the input data for training
the models. Extensive results are presented on various metrics for all
classification and regression models. The results clearly indicate that
CNN-based multivariate forecasting model is the most effective and accurate in
predicting the movement of NIFTY index values with a weekly forecast horizon.

Comments:

The paper will be published in the Proceedings of the "National Conference on Machine Learning and Artificial Intelligence" which will be organized in New Delhi, India, during February 1 - 3, 2020. It contains 7 pages, 3 figures, and 19 tables. arXiv admin note: substantial text overlap with arXiv:1912.07700